Paper summarydennybritzTLDR; The authors train a multilingual Neural Machine Translation (NMT) system based on the Google NMT architecture by prepend a special `2[lang]` (e.g. `2fr`) token to the input sequence to specify the target language. They empirically evaluate model performance on many-to-one, one-to-many and many-to-many translation tasks and demonstrate evidence for shared representations (interlingua).

First published: 2016/11/14 (3 years ago)Abstract: We propose a simple, elegant solution to use a single Neural Machine
Translation (NMT) model to translate between multiple languages. Our solution
requires no change in the model architecture from our base system but instead
introduces an artificial token at the beginning of the input sentence to
specify the required target language. The rest of the model, which includes
encoder, decoder and attention, remains unchanged and is shared across all
languages. Using a shared wordpiece vocabulary, our approach enables
Multilingual NMT using a single model without any increase in parameters, which
is significantly simpler than previous proposals for Multilingual NMT. Our
method often improves the translation quality of all involved language pairs,
even while keeping the total number of model parameters constant. On the WMT'14
benchmarks, a single multilingual model achieves comparable performance for
English$\rightarrow$French and surpasses state-of-the-art results for
English$\rightarrow$German. Similarly, a single multilingual model surpasses
state-of-the-art results for French$\rightarrow$English and
German$\rightarrow$English on WMT'14 and WMT'15 benchmarks respectively. On
production corpora, multilingual models of up to twelve language pairs allow
for better translation of many individual pairs. In addition to improving the
translation quality of language pairs that the model was trained with, our
models can also learn to perform implicit bridging between language pairs never
seen explicitly during training, showing that transfer learning and zero-shot
translation is possible for neural translation. Finally, we show analyses that
hints at a universal interlingua representation in our models and show some
interesting examples when mixing languages.

TLDR; The authors train a multilingual Neural Machine Translation (NMT) system based on the Google NMT architecture by prepend a special `2[lang]` (e.g. `2fr`) token to the input sequence to specify the target language. They empirically evaluate model performance on many-to-one, one-to-many and many-to-many translation tasks and demonstrate evidence for shared representations (interlingua).